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| <p>SQL-on-Hadoop engines provide an |
| execution engine for various data formats and data stores, and |
| many can be made to push down computations down to Druid, while providing a SQL interface to Druid.</p> |
| <p>For a direct comparison between the technologies and when to only use one or the other, things basically comes down to your |
| product requirements and what the systems were designed to do.</p> |
| <p>Druid was designed to</p> |
| <ol> |
| <li>be an always on service</li> |
| <li>ingest data in real-time</li> |
| <li>handle slice-n-dice style ad-hoc queries</li> |
| </ol> |
| <p>SQL-on-Hadoop engines generally sidestep Map/Reduce, instead querying data directly from HDFS or, in some cases, other storage systems. |
| Some of these engines (including Impala and Presto) can be co-located with HDFS data nodes and coordinate with them to achieve data locality for queries. |
| What does this mean? We can talk about it in terms of three general areas</p> |
| <ol> |
| <li>Queries</li> |
| <li>Data Ingestion</li> |
| <li>Query Flexibility</li> |
| </ol> |
| <h3><a class="anchor" aria-hidden="true" id="queries"></a><a href="#queries" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Queries</h3> |
| <p>Druid segments stores data in a custom column format. Segments are scanned directly as part of queries and each Druid server |
| calculates a set of results that are eventually merged at the Broker level. This means the data that is transferred between servers |
| are queries and results, and all computation is done internally as part of the Druid servers.</p> |
| <p>Most SQL-on-Hadoop engines are responsible for query planning and execution for underlying storage layers and storage formats. |
| They are processes that stay on even if there is no query running (eliminating the JVM startup costs from Hadoop MapReduce). |
| Some (Impala/Presto) SQL-on-Hadoop engines have daemon processes that can be run where the data is stored, virtually eliminating network transfer costs. There is still |
| some latency overhead (e.g. serialization/deserialization time) associated with pulling data from the underlying storage layer into the computation layer. We are unaware of exactly |
| how much of a performance impact this makes.</p> |
| <h3><a class="anchor" aria-hidden="true" id="data-ingestion"></a><a href="#data-ingestion" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Data Ingestion</h3> |
| <p>Druid is built to allow for real-time ingestion of data. You can ingest data and query it immediately upon ingestion, |
| the latency between how quickly the event is reflected in the data is dominated by how long it takes to deliver the event to Druid.</p> |
| <p>SQL-on-Hadoop, being based on data in HDFS or some other backing store, are limited in their data ingestion rates by the |
| rate at which that backing store can make data available. Generally, the backing store is the biggest bottleneck for |
| how quickly data can become available.</p> |
| <h3><a class="anchor" aria-hidden="true" id="query-flexibility"></a><a href="#query-flexibility" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Query Flexibility</h3> |
| <p>Druid's query language is fairly low level and maps to how Druid operates internally. Although Druid can be combined with a high level query |
| planner such as <a href="https://github.com/implydata/plywood">Plywood</a> to support most SQL queries and analytic SQL queries (minus joins among large tables), |
| base Druid is less flexible than SQL-on-Hadoop solutions for generic processing.</p> |
| <p>SQL-on-Hadoop support SQL style queries with full joins.</p> |
| <h2><a class="anchor" aria-hidden="true" id="druid-vs-parquet"></a><a href="#druid-vs-parquet" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Druid vs Parquet</h2> |
| <p>Parquet is a column storage format that is designed to work with SQL-on-Hadoop engines. Parquet doesn't have a query execution engine, and instead |
| relies on external sources to pull data out of it.</p> |
| <p>Druid's storage format is highly optimized for linear scans. Although Druid has support for nested data, Parquet's storage format is much |
| more hierarchical, and is more designed for binary chunking. In theory, this should lead to faster scans in Druid.</p> |
| </span></div></article></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/latest/comparisons/druid-vs-spark.html"><span class="arrow-prev">← </span><span>Apache Druid vs Spark</span></a><a class="docs-next button" href="/docs/latest/design/auth.html"><span>Authentication and Authorization</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#druid-vs-parquet">Druid vs Parquet</a></li></ul></nav></div><footer class="nav-footer druid-footer" id="footer"><div class="container"><div class="text-center"><p><a href="/technology">Technology</a> · <a href="/use-cases">Use Cases</a> · <a href="/druid-powered">Powered by Druid</a> · <a href="/docs/latest/latest">Docs</a> · <a href="/community/">Community</a> · <a href="/downloads.html">Download</a> · <a href="/faq">FAQ</a></p></div><div class="text-center"><a title="Join the user group" href="https://groups.google.com/forum/#!forum/druid-user" target="_blank"><span class="fa fa-comments"></span></a> · <a title="Follow Druid" href="https://twitter.com/druidio" target="_blank"><span class="fab fa-twitter"></span></a> · <a title="Download via Apache" href="https://www.apache.org/dyn/closer.cgi?path=/incubator/druid/{{ site.druid_versions[0].versions[0].version }}/apache-druid-{{ site.druid_versions[0].versions[0].version }}-bin.tar.gz" target="_blank"><span class="fas fa-feather"></span></a> · <a title="GitHub" href="https://github.com/apache/druid" target="_blank"><span class="fab fa-github"></span></a></div><div class="text-center license">Copyright © 2019 <a href="https://www.apache.org/" target="_blank">Apache Software Foundation</a>.<br/>Except where otherwise noted, licensed under <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>.<br/>Apache Druid, Druid, and the Druid logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</div></div></footer></div><script type="text/javascript" src="https://cdn.jsdelivr.net/docsearch.js/1/docsearch.min.js"></script><script> |
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